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Most people think NVIDIA is just a producer of computer graphics cards. However, NVIDIA also makes artificial intelligence (AI) programs, robots, and self-driving cars, and soon the company might be synonymous with public safety, all because of its solution system NVIDIA Metropolis.

Yesterday, NVIDIA proposed the creation of an”AI City” using  NVIDIA Metropolis. The premise is quite simple: by 2020, countless video cameras will employ NVIDIA’s Jetson TX2 Module. Cameras will record videos and analyze them with a powerful AI capable of “deep learning” — a highly complex machine learning algorithm that, to my understanding, mimics neural networks and lets the AI learn on its own. The cameras will then send data to data centers/cloud networks that use the NVIDIA Tesla. Ideally, this data will be used for multiple applications, such as helping search for missing children or pets, providing alternate travel routes for GPS systems in real time, and reporting traffic accidents.

According to NVIDIA, cameras without the Jetson TX2 Module can still participate in NVIDIA Metropolis. While the proposed AI cameras can analyze videos on the spot, thus letting them save on bandwidth by only transmitting metadata, regular cameras can still send videos to NVIDIA Jetson or Tesla-powered devices for deep analysis. NVIDIA claims that its GPU-powered system is twenty times more efficient than similar CPU-powered systems.

NVIDIA has already partnered with numerous companies to make NVIDIA Metropolis a reality. These companies include:

  • Aeryon (makes unmanned camera drones)
  • Aqueti (makes the Mantis Camera, a 100 megapixel camera that has fifty times the resolution of an HD camera)
  • Avigilon (develops easy-to-use remote viewing software and HD security cameras)
  • BriefCam (creates a video synopsis technology that reviews videos and can index events by time)
  • Dahua (makes security cameras that can detect and recognize people and cars)
  • Hikvision (makes security cameras that can detect and recognize people and cars)
  • Milestone (develops GPU-powered video management software)
  • MotionDSP (develops programs that analyze videos for forensic evidence)
  • Motionloft (creates real-time person and vehicle-tracking sofware)
  • Netradyne (makes vision-based hardware that aids driver performance)
  • Robotic Assistive Devices (develops autonomous robots that aid in public safety and monitoring)
  • Sensetime (creates face, object, and attribute recognition technology)
  • VIMOC (creates software that captures and processes sensory data)

The NVIDIA Metropolis system has the potential to improve public safety. Only time will tell if people accept it.

All you have to do to get my attention is talk about video games, technology, anime, and/or Dungeons & Dragons - also people in spandex fighting rubber suited monsters.

Artificial Intelligence

A group of humanoid robots from Agility will take care of your spanx

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So far, the humanoid robotics business has only been full of promises and test runs. These programs only use a few robots and don’t usually lead to anything more important, but they are important for the eventual use of new technology. While a pilot with logistics giant GXO went well, Agility announced on Thursday that it has now signed a formal deal.

Moving plastic totes around a Spanx factory in Georgia will be Digit’s first job, and that’s not a lie. The number of two-legged robots that will be taking boxes off of cobots and putting them on conveyor belts has not been made public, so it is likely that it is still too low. When it comes to tens or hundreds of thousands, most people would be happy to share that information.

They are leased as part of a model called “robots-as-a-service” instead of being bought outright. This way, the client can put off paying the huge upfront costs of such a complicated system while still getting support and software updates.

Last year, GXO started to test drive Digit robots. A pilot deal was just announced between the logistics company and Apptronik, one of Agility’s biggest rivals. I’m not sure how one will change the other.

When Peggy Johnson became CEO of Agility in March, she made it clear that the company was focused on ROI. This is a big change in a field where results are still mostly theoretical.

Johnson said, “There will be many firsts in the humanoid robot market in the years to come, but I’m very proud of the fact that Agility is the first company to have real humanoid robots deployed at a customer site, making money and solving real-world business problems.” “Agility has always been focused on the only metric that matters: giving our customers value by putting Digit to work. This milestone deployment sets a new standard for the whole industry.”

It’s not a surprise that Agility, based in Oregon, was the first to reach another important milestone. The company has been ahead of the rest of the market in terms of development and deployment. Of course, the industry is still very new, and there isn’t a clear market leader yet.

Amazon started testing Agility systems in its own warehouses in October of last year, but neither company has said what will happen next.

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Artificial Intelligence

Zuckerberg says that competitors with closed-source AI are trying to “make God”

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In an interview that came out Thursday, Mark Zuckerberg, CEO of Meta, talked about his hopes for the future of AI. He said that he strongly believes there will not be “just one AI.” While talking about how open source can help many people get AI tools, Zuckerberg took a moment to criticize the work of competitors who he didn’t name because he thinks they aren’t being open. He said that these competitors seem to think they are “creating God.”

In a new YouTube interview with Kane Sutter (@Kallaway), Zuckerberg said, “I don’t think that AI technology should be kind of hoarded and… that one company gets to use it to build whatever central, single product that they’re building.”

“It really turns me off when tech people talk about making this ‘one true AI,'” he said. He said, “It’s almost like they think they’re making God or something, but that’s not what we’re doing.” “That’s not how I see this going.”

“I see why, if you’re in an AI lab.” You want to think that what you’re doing is really important, right? It sounds like, “We’re making the one real thing for the future.” But, you know, in real life, that’s not how things work, right?” Zuckerberg talked about it. “It’s not like everyone has just one app on their phone that they use.” Not everyone wants all of their content to come from the same person. People don’t want to buy everything from just one store.

During the talk, Zuckerberg said that many different AIs should be made to capture people’s wide range of interests. On Thursday, the company also announced early tests of its AI Studio software in the U.S. This software will let creators and other people make AI avatars that can message people on Instagram. The AIs will be able to chat with people and answer questions from their followers in a fun way. To avoid confusion, they will be marked as “AI.”

As an example, the CEO of Meta said he didn’t think companies that build closed AI platforms were making the best experiences for people.

He went on, “You want to unlock and…unlock as many people as possible to try new things.” “Well, that’s what culture is, right?” Nobody is letting one group of people tell everyone what to do.

His comments sound a bit like he’s upset because they came out soon after news that Meta had tried to talk to Apple about putting its AIs into Apple’s operating systems instead of just working with OpenAI at launch but was turned down. Bloomberg says that Apple decided not to have formal talks with Meta because it didn’t think Meta’s privacy policies were strong enough.

Without a deal, Meta will not be able to reach the billions of iPhone users that there could be in the world. It looks like Meta’s plan B is to make technology that can be used for more than just smartphones.

During the interview, Zuckerberg talked about the progress the company is making with the Ray-Ban Meta smart glasses. He said that one day, this progress would meet up with the work that is already being done on full holographic displays. But he said the first one will be more popular in the short term.

He said, “I actually think you can have a great experience with cameras, a microphone, speakers, and the ability to do multimodal AI.” This was before the glasses had any kind of display. It also costs less because it doesn’t have a screen. The Meta Quest Pro costs $1,000, while Meta’s smart glasses cost around $300.

Before convergence, Zuckerberg said there would be three different kinds of products: smart glasses without screens, displays that show information on the top of the head, and full holographic displays. He said that one day, people might not have neural interfaces connected to their brains but instead wear a wristband that picks up signals from the brain and lets their hand talk to it. This would let them talk to the neural interface with their hand, which is barely moving. In time, it might also let people type.

Zuckerberg did warn that these kinds of inputs and AI experiences might not be able to replace smartphones right away. “I don’t think that in the history of technology, the new platform has ever made people stop using the old one completely.” “You just don’t use it as much,” he said.

People do things on their phones now that they might have done on their computers 10 to 15 years ago.

He said, “I think that will also happen with glasses.” “We’re not going to give up our phones.” You’ll just keep it in your pocket and only pull it out when you need to use it. But I think more and more people will just say, “Hey, I can take this picture with my glasses on.” The CEO said, “I can ask AI this question or send someone a message; it’s just a lot easier with glasses.”

The speaker said, “I wouldn’t be surprised if, in 10 years, we still have phones, but we’ll probably use them in a much more deliberate way instead of just grabbing them for any technological task we want to do.”

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Artificial Intelligence

What a new study says suggests that ChatGPT may have passed the Turing test

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René Descartes, a French philosopher who may or may not have been high on pot, had an interesting thought in 1637: can a machine think? Alan Turing, an English mathematician and computer scientist, gave the answer to this 300-year-old question in 1950: “Who cares?” He said a better question was what would become known as the “Turing test”: if there was a person, a machine, and a human interrogator, could the machine ever trick the human interrogator into thinking it was the person?

Turing changed the question in this way 74 years ago. Now, researchers at the University of California, San Diego, think they have the answer. A new study that had people talk to either different AI systems or another person for five minutes suggests that the answer might be “yes.”

“After a five-minute conversation, participants in our experiment were no better than random at identifying GPT-4. According to the preprint paper, which has not yet undergone peer review, this suggests that current AI systems can deceive people into believing they are human. “These results probably set a lower bound on how likely it is that someone will lie in more naturalistic settings, where people may not be aware of the possibility of lying or only focus on finding it.”

Even though this is a big event that makes headlines, it’s not a milestone that everyone agrees on. The researchers say that Turing first thought of the imitation game as a way to test intelligence, but “many objections have been raised to this idea.” People, for example, are known for being able to humanize almost anything. We want to connect with things, whether they’re people, dogs, or a Roomba with googly eyes on top of it.

Also, it’s interesting that ChatGPT-4 and ChatGPT-3.5, which was also tested, only persuaded humans that it was a person about half of the time, which isn’t much better than random chance. What does this result really mean?

As it turns out, ELIZA was one of the AI systems that the team built into the experiment as a backup plan. She was made at MIT in the mid-1960s and was one of the first programs of her kind. She was impressive for her time, but she doesn’t have much to do with modern large-language model-based systems or LLM-based systems.

“ELIZA could only give pre-written answers, which greatly limited what it could do. Live Science talked to Nell Watson, an AI researcher at the Institute of Electrical and Electronics Engineers (IEEE), about how it might fool someone for five minutes but soon show its flaws. “Language models are completely adaptable; they can put together answers to a lot of different topics, speak in specific languages or sociolects, and show who they are by displaying personality and values that are based on their characters.” a significant improvement over something that a person, no matter how intelligent and careful they were, programmed by hand.

She was perfect for the experiment because she was the same as everyone else. How do you explain test subjects who are lazy and pick between “human” and “machine” at random? If ELIZA gets the same score as chance, then the test is probably not being taken seriously because she’s not that good. In what way can you tell how much of the effect is just people giving things human traits? How much did ELIZA get them to change their minds? That much is probably how much it is.

In fact, ELIZA got only 22%, which is just over 1 in 5 people believing she was human. It’s more likely that ChatGPT has passed the Turing test now that test subjects could reliably tell the difference between some computers and people, but not ChatGPT, the researchers write.

So, does this mean we’re entering a new era of AI that acts like humans? Are computers smarter than people now? Maybe, but we probably shouldn’t make our decisions too quickly.

The researchers say, “In the end, it seems unlikely that the Turing test provides either necessary or sufficient evidence for intelligence. At best, it provides probabilistic support.” The people who took part weren’t even looking for what you might call “intelligence”; the paper says they “were more focused on linguistic style and socio-emotional factors than more traditional notions of intelligence such as knowledge and reasoning.” This “could reflect interrogators’ latent assumption that social intelligence has become the human trait that is most difficult for machines to copy.”

Which brings up a scary question: is the fall of humans the bigger problem than the rise of machines?

“Real humans were actually more successful, convincing interrogators that they were human two-thirds of the time,” the paper’s co-author, Cameron Jones, told Tech Xplore. “Our results suggest that in the real world, people might not be able to reliably tell if they’re talking to a human or an AI system.”

“In the real world, people might not be as aware that they’re talking to an AI system, so the rate of lying might be even higher,” he warned. “This makes me wonder what AI systems will be used for in the future, whether they are used to do bots, do customer service jobs, or spread fake news or fraud.”

There is a draft of the study on arXiv, but it has not yet been reviewed by other scientists.

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